The invention relates in one aspect to a computer-implemented method for predicting the binding of at least one
protein of interest, preferably at least one
receptor,
antibody,
antibody fragment or equivalents thereof, or DARPin, to
a peptide of interest, comprising a. providing sequence and / or structural data of at least one reference
peptide, optionally providing sequence and / or structural data of at least one HLA molecule and / or of at least one HLA-
peptide complex (pHLA), and / or the at least one reference
peptide presented within a pHLA complex, b. providing binding data, preferably comprising the
dissociation constant (KD), of the at least one reference peptide and / or the at least one reference peptide presented within a HLA-
peptide complex (pHLA) with at least one
protein of interest, c. training a
machine learning (ML)-based model or
artificial intelligence (AI) based on the data provided in a. and the binding data provided in b., d. providing sequence and / or structural data of at least one peptide of interest, e. employing the
machine learning (ML)- based model or
artificial intelligence (AI) trained in c. to predict the binding or interaction properties of the at least one peptide of interest, preferably when presented within a pHLA complex, to the at least one
protein of interest.